Bibliometric Analysis of Scientific Studies Published on Game Customer Churn Analysis Between 2008 and 2022

Yıl 2022, Cilt: 5 Sayı: 1, 55 - 75, 30.06.2022

Kaan Arık Murat Gezer Seda Tolun Tayalı

Öz

Churn prediction has become a key part of many modern businesses because of the performance advantages it brings. It may assist businesses in determining measures such as customer retention and revenue production. Business intelligence (BI) is the process of transforming data into actionable insights that help a company make better choicesThis research includes a bibliometric analysis of works on game analytics, particularly customer churn in gaming business. Our research is mostly focused on identifying the content and goals of studies that have already been published. In game analytics, classification, grouping, and statistical analysis approaches are utilized to acquire insight via the study of studies. Keywords were analyzed in articles, papers, books, and research materials on gaming analytics and customer churn analysis. Then there's a look at which measures are most important in game analytics, as well as some of the most often utilized artificial intelligence systems. These difficulties are examined in the assessment section, where performance evaluation measures are more significant in this sector. For bibliometric analysis, the keyword "game churn analysis" was used. Data from Google Scholar, as well as Publish or Perish and Zotero. Year requirements for bibliometric analysis were set between 2008 and 2022. The study data analysis tools include Excel 2019 and Vosviewer. Finally, we analyzed databases, widely used data sets, game titles, metric kinds, indexing databases, countries where studies were published, categories of scientific study, word and author bibliometric maps, and algorithms.

Anahtar Kelimeler

bibliometric analysis, churn analysis, game analytics, game data mining, literature review

2008-2022 Yılları Arasında Oyun Müşteri Kaybı Analizi Üzerine Yayımlanan Bilimsel Çalışmaların Bibliyometrik Analizi

Öz

Kayıp tahmini, getirdiği performans avantajları nedeniyle birçok modern işletmenin önemli bir parçası haline gelmiştir. İşletmelere müşteriyi elde tutma ve gelir üretimi gibi önlemleri belirlemede yardımcı olmaktadır. İş zekası (BI), bir şirketin daha iyi seçimler yapmasına yardımcı olan verileri eyleme dönüştürülebilir içgörülere dönüştürme sürecidir. Araştırmamız çoğunlukla daha önce yayınlanmış çalışmaların içeriğini ve hedeflerini belirlemeye odaklanmıştır. Oyun içi analitik, sınıflandırma, gruplama ve istatistiksel analiz yaklaşımları, çalışmaların incelenmesi yoluyla içgörü elde edilmeye çalışılmıştır. Anahtar kelimeler, oyun analizi ve müşteri kaybı analizi üzerine makaleler, makaleler, kitaplar ve araştırma materyallerinde analiz edilmiştir. Ardından, oyun analitiğinde hangi önlemlerin en önemli olduğuna ve ayrıca en sık kullanılan yapay zeka sistemlerinden bazılarına bir bakış açısı aranmıştır. Bu zorluklar, bu sektörde performans değerlendirme ölçütlerinin daha önemli olduğu değerlendirme bölümünde incelenmiştir. Bibliyometrik analiz için "oyun kayıp analizi" anahtar kelimesi kullanılmıştır. Google Akademik'in yanı sıra Publish or Perish ve Zotero'dan gelen veriler. Bibliyometrik analiz için yıl aralığı 2008 ile 2022 arasında belirlendi. Çalışma verisi analizi için Excel 2019 ve Vosviewer'ı araçları kullanıldı. Son olarak, veri tabanları, yaygın olarak kullanılan veri kümeleri, oyun başlıkları, metrik türleri, indeksleme veritabanları, çalışmaların yayınlandığı ülkeler, bilimsel çalışma kategorileri, kelime ve yazar bibliyometrik haritaları ve algoritmaları analiz edilmiş ve sonuç kısmında tartışılmıştır.

Anahtar Kelimeler

bibliyometrik analiz, kayıp analizi, oyun analitiği, oyun veri madenciliği, literatür taraması

Kaynakça

• [1] M. And, Oyun ve bügü: Türk kültüründe oyun kavramı, Genişletilmiş baskı. İstanbul: YKY, 2003.
• [2] C. S. Ang and P. Zaphiris, “Computer Games and Language Learning:,” in Handbook of Research on Instructional Systems and Technology, T. T. Kidd and H. Song, Eds. IGI Global, 2008, pp. 449–462. doi: 10.4018/978-1-59904-865-9.ch032.
• [3] E. M. Avedon and B. Sutton-Smith, The study of games. New York, N.Y. [u.a]: Ishi Press, 2015.
• [4] G. Costikyan, “I have no words & I must design: toward a critical vocabulary for games,” in Proceedings of the computer games and digital cultures conference, Finland, 2002, pp. 9–33.
• [5] R. E. Cardona-Rivera, J. P. Zagal, and M. S. Debus, “GFI: A Formal Approach to Narrative Design and Game Research,” in Interactive Storytelling, vol. 12497, A.-G. Bosser, D. E. Millard, and C. Hargood, Eds. Cham: Springer International Publishing, 2020, pp. 133–148. doi: 10.1007/978-3-030-62516-0_13.
• [6] J. Juul, “Games telling stories? A brief note on games and narratives,” Game studies, vol. 1, no. 1, pp. 1–12, 2001.
• [7] Kevin Maroney, “My Entire Waking Life,” http://www.thegamesjournal.com/, 2011. http://www.thegamesjournal.com/articles/MyEntireWakingLife.shtml (accessed Apr. 13, 2022).
• [8] R. A. Myers and G. Mertz, “The Limits of Exploitation: A Precautionary Approach,” Ecological Applications, vol. 8, no. 1, p. S165, Feb. 1998, doi: 10.2307/2641375.
• [9] S. Roohi, A. Relas, J. Takatalo, H. Heiskanen, and P. Hämäläinen, “Predicting Game Difficulty and Churn Without Players,” arXiv:2008.12937 [cs], Aug. 2020, doi: 10.1145/3410404.3414235.
• [10] R. J. Paddick, “The Grasshopper: Games, Life and Utopia. By Bernard Suits. Toronto, University of Toronto Press 1978,” Journal of the Philosophy of Sport, vol. 6, no. 1, pp. 73–78, Jan. 1979, doi: 10.1080/00948705.1979.10654153.
• [11] G. Tavinor, “Definition of videogames,” Contemporary Aesthetics (Journal Archive), vol. 6, no. 1, p. 16, 2008.
• [12] N. Whitton, Digital Games and Learning: Research and Theory, 0 ed. Routledge, 2014. doi: 10.4324/9780203095935.
• [13] K. S. Tekinbaş and E. Zimmerman, Rules of play: game design fundamentals. Cambridge, Mass: MIT Press, 2003.
• [14] J. Huizinga, Homo ludens: a study of the play-element in culture. Kettering, OH: Angelico Press, 2016.
• [15] C. Crawford, Chris Crawford on game design. Indianapolis, Ind: New Riders, 2003.
• [16] M. Prensky, “Digital natives, digital immigrants part 2: Do they really think differently?,” On the horizon, 2001.
• [17] Tom Wijman, “The Games Market and Beyond in 2021: The Year in Numbers,” Newzoo. https://newzoo.com/insights/articles/the-games-market-in-2021-the-year-in-numbers-esports-cloud-gaming/ (accessed Apr. 11, 2022).
• [18] J. H. Lee, N. Karlova, R. I. Clarke, K. Thornton, and A. Perti, “Facet Analysis of Video Game Genres,” presented at the iConference 2014 Proceedings: Breaking Down Walls. Culture - Context - Computing, Mar. 2014. doi: 10.9776/14057.
• [19] K. Bergstrom, “Moving Beyond Churn: Barriers and Constraints to Playing a Social Network Game,” Games and Culture, vol. 14, no. 2, pp. 170–189, Mar. 2019, doi: 10.1177/1555412018791697.
• [20] R. Chikhani, “The History Of Gaming: An Evolving Community | TechCrunch,” Techcrunch, Oct. 31, 2015. https://techcrunch.com/2015/10/31/the-history-of-gaming-an-evolving-community/ (accessed May 01, 2022).
• [21] F. Burstein, F. Burstein, and C. W. Holsapple, Handbook on decision support systems. Berlin [London]: Springer, 2008.
• [22] H. P. Luhn, “A Business Intelligence System,” IBM Journal of Research and Development, vol. 2, no. 4, pp. 314–319, Oct. 1958, doi: 10.1147/rd.24.0314.
• [23] É. Foley and M. G. Guillemette, “What is Business Intelligence?,” International Journal of Business Intelligence Research, vol. 1, no. 4, pp. 1–28, Oct. 2010, doi: 10.4018/jbir.2010100101.
• [24] S. Butler, “Customer Relationships: Changing the Game: CRM in the e‐World,” Journal of Business Strategy, vol. 21, no. 2, pp. 13–14, Feb. 2000, doi: 10.1108/eb040067.
• [25] C. Gold and T. Tzuo, Fighting churn with data: the science and strategy of customer retention. Shelter Island: Manning, 2020.
• [26] J. Ding, D. Gao, and X. Chen, “Alone in the game: Dynamic Spread of Churn Behavior in a Large Social Network a Longitudinal Study in MMORPG,” IJSH, vol. 9, no. 3, pp. 35–44, Mar. 2015, doi: 10.14257/ijsh.2015.9.3.04.
• [27] D. García, À. Nebot, and A. Vellido, “Intelligent data analysis approaches to churn as a business problem: a survey,” Knowledge and Information Systems, vol. 51, pp. 1–56, Jun. 2017, doi: 10.1007/s10115-016-0995-z.
• [28] P. Zackariasson and T. L. Wilson, Eds., The video game industry: formation, present state, and future, 1. published. New York, NY; London: Routledge, 2012.
• [29] Z. H. Borbora and J. Srivastava, “User Behavior Modelling Approach for Churn Prediction in Online Games,” in 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, Amsterdam, Netherlands, Sep. 2012, pp. 51–60. doi: 10.1109/SocialCom-PASSAT.2012.84.
• [30] A. Vítek, “Cross-Game Modeling of Player’s Behaviour in Free-To-Play Games,” in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, New York, NY, USA: Association for Computing Machinery, 2020, pp. 384–387. Accessed: Apr. 25, 2022. [Online]. Available: https://doi.org/10.1145/3340631.3398677
• [31] K. Rothmeier, N. Pflanzl, J. A. Hüllmann, and M. Preuss, “Prediction of Player Churn and Disengagement Based on User Activity Data of a Freemium Online Strategy Game,” IEEE Transactions on Games, vol. 13, no. 1, pp. 78–88, Mar. 2021, doi: 10.1109/TG.2020.2992282.
• [32] M. S. El-Nasr, A. Drachen, and Alessandro Canossa, Eds., Game analytics: maximizing the value of player data. New York: Springer, 2013.
• [33] J. Qiu, R. Zhao, S. Yang, and K. Dong, Informetrics: Theory, Methods and Applications. New York, NY, 2017.
• [34] W. Hulme, “Statistical Bibliography in Relation to the Growth of Modern Civilization: Two Lectures delivered in the University of Cambridge in May 1922,” Nature, vol. 112, no. 2816, pp. 585–586, Oct. 1923, doi: 10.1038/112585a0.
• [35] W. Glänzel, “Bibliometrics as a research field: A course on theory and application of bibliometric indicators,” Course Handouts, Jan. 2003.
• [36] M. N. Kurutkan and F. Orhan, Sağlık Politikası Konusunun Bilim Haritalama Teknikleri ile Analizi. Türkiye, Adıyaman: İksad Yayınevi, 2018.
• [37] P. Bertens, A. Guitart, and A. Perianez, “Games and big data: A scalable multi-dimensional churn prediction model,” in 2017 IEEE Conference on Computational Intelligence and Games (CIG), New York, NY, USA, Aug. 2017, pp. 33–36. doi: 10.1109/CIG.2017.8080412.
• [38] P. Bertens, A. Guitart, P. P. Chen, and A. Perianez, “A Machine-Learning Item Recommendation System for Video Games,” in 2018 IEEE Conference on Computational Intelligence and Games (CIG), Aug. 2018, pp. 1–4. doi: 10.1109/CIG.2018.8490456.
• [39] E. Lee et al., “Game Data Mining Competition on Churn Prediction and Survival Analysis Using Commercial Game Log Data,” IEEE Transactions on Games, vol. 11, no. 3, pp. 215–226, Sep. 2019, doi: 10.1109/TG.2018.2888863.
• [40] Á. Periáñez, A. Saas, A. Guitart, and C. Magne, “Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles,” 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 564–573, Oct. 2016, doi: 10.1109/DSAA.2016.84.
• [41] R. Sifa, “Predicting Player Churn with Echo State Networks,” in 2021 IEEE Conference on Games (CoG), Aug. 2021, pp. 1–5. doi: 10.1109/CoG52621.2021.9619059.
• [42] C. Bauckhage, A. Drachen, and R. Sifa, “Clustering Game Behavior Data,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 7, no. 3, pp. 266–278, Sep. 2015, doi: 10.1109/TCIAIG.2014.2376982.
• [43] A. Drachen et al., “Rapid prediction of player retention in free-to-play mobile games,” in Proceedings of the Twelfth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Burlingame, California, USA, Oct. 2016, pp. 23–29.
• [44] A. Drachen et al., “To be or not to be...social: incorporating simple social features in mobile game customer lifetime value predictions,” in Proceedings of the Australasian Computer Science Week Multiconference, New York, NY, USA, Jan. 2018, pp. 1–10. doi: 10.1145/3167918.3167925.
• [45] F. Hadiji, R. Sifa, A. Drachen, C. Thurau, K. Kersting, and C. Bauckhage, “Predicting player churn in the wild,” in 2014 IEEE Conference on Computational Intelligence and Games, Dortmund, Germany, Aug. 2014, pp. 1–8. doi: 10.1109/CIG.2014.6932876.
• [46] M. Tamassia, W. Raffe, R. Sifa, A. Drachen, F. Zambetta, and M. Hitchens, “Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game,” in 2016 IEEE Conference on Computational Intelligence and Games (CIG), Sep. 2016, pp. 1–8. doi: 10.1109/CIG.2016.7860431.
• [47] E.-B. Lee, J. Kim, and S.-G. Lee, “Predicting customer churn in mobile industry using data mining technology,” Industrial Management & Data Systems, vol. 117, no. 1, pp. 90–109, Jan. 2017, doi: 10.1108/IMDS-12-2015-0509.
• [48] S.-K. Lee, S.-J. Hong, S.-I. Yang, and H. Lee, “Predicting churn in mobile free-to-play games,” in 2016 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Oct. 2016, pp. 1046–1048. doi: 10.1109/ICTC.2016.7763364.
• [49] S.-K. Lee, S.-J. Hong, S.-I. Yang, and H. Lee, “Predicting churn in mobile free-to-play games,” in 2016 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2016, pp. 1046–1048. doi: 10.1109/ICTC.2016.7763364.
• [50] S. Kim, D. Choi, E. Lee, and W. Rhee, “Churn prediction of mobile and online casual games using play log data,” PLOS ONE, vol. 12, no. 7, p. e0180735, Jul. 2017, doi: 10.1371/journal.pone.0180735.
• [51] J. Ahn, J. Hwang, D. Kim, H. Choi, and S. Kang, “A Survey on Churn Analysis in Various Business Domains,” IEEE Access, vol. 8, pp. 220816–220839, 2020, doi: 10.1109/ACCESS.2020.3042657.
• [52] M. Beirne-Smith, J. R. Patton, and S. H. Kim, Mental retardation: an introduction to intellectual disabilities, 7th ed. Upper Saddle River, N.J: Pearson Merrill Prentice Hall, 2006.
• [53] K. Y. Hwan, Y. S. Il, and K. H. Kang, “Correlation Analysis between Game Bots and Churn using Access Record,” Journal of Korea Game Society, vol. 18, no. 5, pp. 47–58, 2018, doi: 10.7583/JKGS.2018.18.5.47.